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Biomedicines
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Neural Nexus: Interdisciplinary Perspectives on Neurological Disorders
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Neural Nexus: Interdisciplinary Perspectives on Neurological Disorders

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Neurobiology and Clinical Neuroscience".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 987

Special Issue Editors

Dr. Gianandrea Traversi

E-Mail Website
Guest Editor
Unit of Medical Genetics, Department of Laboratory Medicine, Ospedale Isola Tiberina-Gemelli Isola, 00186 Rome, Italy
Interests: neuroscience; genetics; neurological disorders; biomarkers; therapeutic targets; neuroscience research
Dr. Marianna Mazza

E-Mail Website
Guest Editor
1. Department of Neurosciences, Università Cattolica del Sacro Cuore, Rome, Italy
2. Unit of Psychiatry, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
Interests: mood disorders; anxiety; psychosis; personality disorders; cardiovascular disorders; clinical psychopharmacology; psychiatric emergencies; peripartum; interpersonal violence
Special Issues, Collections and Topics in MDPI journals
Dr. Giuseppe Marano

E-Mail Website
Guest Editor
1. Department of Neurosciences, Università Cattolica del Sacro Cuore, Rome, Italy
2. Unit of Psychiatry, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
Interests: mental health; neurobiology neuroscience; women's health; clinical trials; bipolar disorder; mood disorders translation science; research methods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Neurological disorders present a significant global health burden, affecting millions of individuals worldwide. From the debilitating effects of Alzheimer's disease to the complexities of Parkinson's disease, these conditions pose substantial challenges to individuals, families, and healthcare systems alike. Despite significant advancements in research, numerous neurological disorders remain poorly understood and difficult to treat effectively.

The integration of artificial intelligence and machine learning has revolutionized neuroscience research, enabling scientists to analyze vast datasets and identify novel biomarkers. These technologies offer immense potential for improving the diagnosis, prognosis, and treatment of neurological diseases.

Achieving a comprehensive overview of the latest advancements and emerging trends in the field of neurological disorders is crucial. Bringing together contributions from leading researchers in the fields of neuroscience, neurology, psychology, and related disciplines fosters interdisciplinary dialogue and promotes innovative methods for understanding and treating neurological conditions.

Dr. Gianandrea Traversi
Dr. Marianna Mazza
Dr. Giuseppe Marano
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomedicines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • neurological disorders
  • mental health
  • artificial intelligence
  • machine learning
  • biomarkers
  • drug discovery
  • therapeutic targets
  • neuroscience research

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Published Papers (1 paper)

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Research

18 pages, 4041 KB  
Article
A Deep Learning Approach to Alzheimer’s Diagnosis Using EEG Data: Dual-Attention and Optuna-Optimized SVM
by Funda Bulut Arikan, Dilber Cetintas, Aziz Aksoy and Muhammed Yildirim
Biomedicines 2025, 13(8), 2017; https://doi.org/10.3390/biomedicines13082017 - 19 Aug 2025
Viewed by 609
Abstract
Background/Objectives: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, pathologically defined by the accumulation of amyloid-β plaques and tau-related neurofibrillary tangles in the brain. It represents a principal driver of cognitive deterioration in middle-aged and elderly populations. Early diagnosis and pharmacological management [...] Read more.
Background/Objectives: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, pathologically defined by the accumulation of amyloid-β plaques and tau-related neurofibrillary tangles in the brain. It represents a principal driver of cognitive deterioration in middle-aged and elderly populations. Early diagnosis and pharmacological management of the disease markedly improve both the quality and duration of life. Methods: Electroencephalography (EEG) is critical in detecting and analyzing Alzheimer’s disease. The widespread use of mobile EEG devices in recent years has necessitated real-time and effective data processing. However, extracting disease-specific features from EEG data still poses a significant challenge, especially in cases that must be completed quickly. This study aims to determine the frequency bands associated with Alzheimer’s disease in EEG data obtained from multiple channels and to accelerate the detection methods. An accurate classification that requires little computation is the primary goal. Results: EEG recordings of 48 individuals (24 AD and 24 healthy controls (HC)) obtained from Florida State University were divided into Alpha, Beta, Delta, Gamma, and Theta frequency bands; scalograms and spectrograms were generated for each frequency band. The effectiveness of these bands was evaluated using the MobileNetV2 architecture. The results showed that Delta and Beta frequency bands were the most significant for Alzheimer’s detection. By analyzing the features obtained from the Delta and Beta bands using the MobileNetV2 model integrated with the Dual-Attention Mechanism, it was determined that the attention mechanisms improved model performance by 2%. In addition, the use of an SVM classifier with hyperparameters optimized via Optuna resulted in approximately 3% performance improvement, suggesting that hyperparameter tuning may contribute positively to classification accuracy. Furthermore, combining features obtained from these frequency bands increased the detection performance when evaluated with larger datasets. Conclusions: The study demonstrates the potential of frequency band-based analyses and feature fusion methods to increase the accuracy and efficiency of Alzheimer’s diagnosis using EEG data. The results are promising; however, they should be interpreted with caution regarding their generalizability. Full article
(This article belongs to the Special Issue Neural Nexus: Interdisciplinary Perspectives on Neurological Disorders)
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